We investigate the observed trends and interannual variability in surface ozone over the United States using the Global Modeling Initiative chemical transport model. We discuss the roles of meteorology, emissions, and transport from the stratosphere in driving the interannual variability in different regions and seasons. We demonstrate that a hindcast simulation for 1991-2010 can reproduce much of the observed variability and the trends in summertime ozone, with correlation coefficients for seasonally and regionally averaged median ozone ranging from 0.46 to 0.89. Reproducing the interannual variability in winter and spring in the western United States may require higher-resolution models to adequately represent stratosphere-troposphere exchange. Hindcast simulations with fixed versus variable emissions show that changes in anthropogenic emissions drive the observed negative trends in monthly median ozone concentrations in the eastern United States during summer, as well as the observed reduction in the amplitude of the seasonal cycle. The simulation underestimates positive trends in the western United States during spring, but excluding the first 4 years of data removes many of the statistically significant trends in this region. The reduction in the slope of the ozone versus temperature relationship before and after major emission reductions is also well represented by the model. Our results indicate that a global model can reproduce many of the important features of the meteorologically induced ozone variability as well as the emission-driven trends, lending confidence to model projections of future changes in regional surface ozone.

[1] The impact of nitric oxide (NO) emissions by lightning on summertime North American nitrogen oxides (NO x ) and ozone is studied using the Global Modeling Initiative (GMI) CTM and an improved lightning NO algorithm. The spatial distributions of modeled and National Lightning Detection Network-based flash rates during the summers of 2004-2006 agree well (R 2 = 0.49, 18% low bias). Despite this reasonable agreement, 9-12 km model NO x during the Intercontinental Chemical Transport Experiment (INTEX-A) campaign is a factor of 2.2-3.6 too low for a simulation that includes a 480 mol per flash midlatitude lightning NO source, the source that provides the best agreement with measurements. Possible causes of this low bias include biases in model convection and/or too rapid NO x chemistry in the upper troposphere. Model tropospheric NO 2 columns over the southeastern United States during these summers show a 7% high bias with respect to the OMI DOMINO/GEOS-Chem tropospheric column NO 2 product.

Biomass burning in the tropics is set intentionally during dry season each year to destroy agricultural waste and clear land for human expansion. These burning activities cause pollution including atmospheric particulates and trace gases which are harmful to human health. Measurements from the Aura Ozone Monitoring Instrument (OMI) and Microwave Limb Sounder (MLS) from October 2004–November 2008 are used to evaluate the effects of biomass burning on tropical tropospheric ozone in the context of the Global Modeling Initiative (GMI) chemical transport model. The impact of biomass burning on ozone is significant within and near the burning regions with increases of ∼10–25% in tropospheric column ozone relative to average background concentrations. Globally the model indicates increases of ∼4–5% in ozone, ∼7–9% in NOx (NO + NO2), and ∼30–40% in CO.

Abstract. We use a series of chemical transport model and chemistry climate model simulations to investigate the observed negative trends in MOPITT CO over several regions of the world, and to examine the consistency of time-dependent emission inventories with observations. We find that simulations driven by the MACCity inventory, used for the Chemistry Climate Modeling Initiative (CCMI), reproduce the negative trends in the CO column observed by MOPITT for 2000–2010 over the eastern United States and Europe. However, the simulations have positive trends over eastern China, in contrast to the negative trends observed by MOPITT. The model bias in CO, after applying MOPITT averaging kernels, contributes to the model–observation discrepancy in the trend over eastern China. This demonstrates that biases in a model's average concentrations can influence the interpretation of the temporal trend compared to satellite observations. The total ozone column plays a role in determining the simulated tropospheric CO trends. A large positive anomaly in the simulated total ozone column in 2010 leads to a negative anomaly in OH and hence a positive anomaly in CO, contributing to the positive trend in simulated CO. These results demonstrate that accurately simulating variability in the ozone column is important for simulating and interpreting trends in CO.